Below, you will find suggested resources (books, websites, videos, etc) on relevant topics
Dunne, Fionn, and Nik Petrinic. Introduction to computational plasticity. OUP Oxford, 2005.
This book is an excellent starting point for researchers new to the field of computational plasticity. It clearly explains the fundamental concepts and provides practical insights. Key highlights include classical constitutive models implemented as ABAQUS UMATs, such as the isotropic elastic model, Von Mises model (covering isotropic, kinematic, and combined hardening), superplasticity, and cyclic plasticity.
Simo, Juan C., and Thomas JR Hughes. Computational inelasticity. Vol. 7. Springer Science & Business Media, 2006.
Authored by pioneers in modern computational mechanics, this comprehensive book covers a wide range of topics essential for anyone seeking a deep understanding of computational mechanics. It’s an invaluable resource that should not be missed by those aiming to gain a thorough grasp of the subject.
Simo, Juan C. Numerical analysis and simulation of plasticity. Handbook of numerical analysis 6 (1998): 183-499.
This work by Dr. Simo offers detailed discussions on various numerical algorithms related to plasticity. It is particularly valuable for those interested in the numerical aspects of computational mechanics, providing in-depth analysis and simulations that are crucial for advanced research in the field.
Belytschko, Jacob Fish Ted. A first course in finite elements. 2007.
Bathe K-J. Finite Element Procedures. Second edition. [Klaus-Jürgen Bathe]; 2014.
Lecture video by Dr. Bathe: https://www.youtube.com/watch?v=oNqSzzycRhw&list=PLD4017FC423EC3EB5; https://www.youtube.com/watch?v=TJh7KPABk6I&list=PL75C727EA9F6A0E8B
Liu, Wing Kam, Shaofan Li, and Harold S. Park. Eighty years of the finite element method: Birth, evolution, and future. Archives of Computational Methods in Engineering 29.6 (2022): 4431-4453.
Softwares for uncertainty quantification: UQlab (https://www.uqlab.com/), UQpy (https://uqpyproject.readthedocs.io/en/latest/index.html#)
Der Kiureghian, Armen. Structural and system reliability. Cambridge University Press, 2022.
Robert, Christian P., George Casella, and George Casella. Monte Carlo statistical methods. Vol. 2. New York: Springer, 1999.
Johnson, Richard A., Irwin Miller, and John E. Freund. Probability and statistics for engineers. Vol. 2000. London: Pearson Education, 2000.
A Beginner's Guide to Structural Engineering by T. Bart Quimby, P.E., Ph.D., F.ASCE: https://www.bgstructuralengineering.com/
A visualization tool to interpret the Gaussian process: http://www.infinitecuriosity.org/vizgp/
A Youtube channel to explain everything in Engineering: https://www.youtube.com/@TheEfficientEngineer
How to learn and interpret complex concepts in Engineering using the Feynman Technique: https://fs.blog/feynman-technique/
Machine learning video library by Dr. Abu-Mostafa: https://home.work.caltech.edu/library/
Find papers using connected papers: https://www.connectedpapers.com/
Lecture notes on statistics, machine learning in University of Waterloo: https://wiki.math.uwaterloo.ca/statwiki/index.php?title=main_Page